Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2003
Abstract
Two methods of transforming the Weibull data to near normality, namely the Box-Cox method and Kullback-Leibler (KL) information method, are discussed and contrasted. A simple prediction interval (PI) based on the better KL information method is proposed. The asymptotic property of this interval is established. Its small sample behavior is investigated using Monte Carlo simulation. Simulation results show that this simple interval is close to the existing complicated PI where the percentage points of the reference distribution have to be either simulated or approximated.
Keywords
Box-Cox transformation, Coverage probability, Kullback-Leibler information, Prediction interval, Weibull distribution
Discipline
Econometrics
Research Areas
Econometrics
Publication
Computational Statistics and Data Analysis
Volume
43
Issue
3
First Page
357
Last Page
368
ISSN
0167-9473
Identifier
10.1016/s0167-9473(02)00232-3
Publisher
Elsevier
Citation
YANG, Zhenlin; SEE, Stanley P.; and XIE, Min.
Transformation Approaches for the Construction of Weibull Prediction Interval. (2003). Computational Statistics and Data Analysis. 43, (3), 357-368.
Available at: https://ink.library.smu.edu.sg/soe_research/196
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/s0167-9473(02)00232-3